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| 15. |  | ASSAD, E. D.; PINTO, H. S.; ZULLO JUNIOR, J.; ÁVILA, A. M. H. Impacto das mudanças climáticas no zoneamento agroclimático do café no Brasil. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 39, n. 11, p. 1057-1064, nov. 2004.| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Unidades Centrais. |
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| Registros recuperados : 54 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
06/08/2009 |
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Data da última atualização: |
31/01/2020 |
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Tipo da produção científica: |
Artigo em Anais de Congresso / Nota Técnica |
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Autoria: |
ROMANI, L. A. S.; TRAINA, A. J. M.; SOUSA, E. P. M. de; ZULLO JÚNIOR, J.; AVILA, A. M. H.; RODRIGUES JR. J. F.; TRAINA JÚNIOR. C. |
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Afiliação: |
LUCIANA ALVIM SANTOS ROMANI, CNPTIA; AGMA J. M. TRAINA, Ciência da Computação/USP São Carlos; ELAINE P. M. DE SOUSA, Ciência da Computação/USP São Carlos; JURANDIR ZULLO JÚNIOR, CEPAGRI/ UNICAMP; ANA M. H. AVILA, CEPAGRI/UNICAMP; JOSE FERNANDO RODRIGUES JR., UFSCAR; CAETANO TRAINA JÚNIOR, Ciência da Computação/USP São Carlos. |
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Título: |
Computational framework to analyze agrometeorological, climate and remote sensing data: challenges and perspectives. |
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Ano de publicação: |
2009 |
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Fonte/Imprenta: |
In: CONGRESSO DA SOCIEDADE BRASILEIRA DE COMPUTAÇÃO, 29., 2009, Bento Gonçalves. Anais... Rio Grande do SUL: Instituto de Informática UFRGS. |
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Páginas: |
p. 323-337. |
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Idioma: |
Inglês |
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Notas: |
CSBC 2009. |
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Conteúdo: |
In the past few years, improvements in the data acquisition technology have decreased the time interval of data gathering. Consequently, institutions have stored huge amounts of data such as climate time series and remote sensing images. Computational models to filter, transform, merge and analyze data from many different areas are complex and challenging. The complexity increases even more when combining several knowledge domains. Examples are research in climatic changes, biofuel production and environmental problems. A possible solution to the problem is the association of several computational techniques. Accordingly, this paper presents a framework to analyze, monitor and visualize climate and remote sensing data by employing methods based on fractal theory, data mining and visualization techniques. Initial experiments showed that the information and knowledge discovered from this framework can be employed to monitor sugar cane crops, helping agricultural entrepreneurs to make decisions in order to become more productive. Sugar cane is the main source to ethanol production in Brazil, and has a strategic importance for the country economy and to guarantee the Brazilian self-sufficiency in this important, renewable source of energy. |
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Palavras-Chave: |
Cana-de-açúcar; Dados agrometeorológicos; Dados climáticos; Dados de sensoriamento remoto; Dados massivos; Data mining; Mineração de dados; Séries temporais; Técnicas de visualização; Teoria dos fractais. |
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Thesagro: |
Agricultura. |
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Thesaurus NAL: |
Agriculture; Remote sensing; Sugarcane. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/13655/1/ROMANI_2009.pdf
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|
Marc: |
LEADER 02488nam a2200373 a 4500
001 1256494
005 2020-01-31
008 2009 bl uuuu u00u1 u #d
100 1 $aROMANI, L. A. S.
245 $aComputational framework to analyze agrometeorological, climate and remote sensing data$bchallenges and perspectives.$h[electronic resource]
260 $aIn: CONGRESSO DA SOCIEDADE BRASILEIRA DE COMPUTAÇÃO, 29., 2009, Bento Gonçalves. Anais... Rio Grande do SUL: Instituto de Informática UFRGS.$c2009
300 $ap. 323-337.
500 $aCSBC 2009.
520 $aIn the past few years, improvements in the data acquisition technology have decreased the time interval of data gathering. Consequently, institutions have stored huge amounts of data such as climate time series and remote sensing images. Computational models to filter, transform, merge and analyze data from many different areas are complex and challenging. The complexity increases even more when combining several knowledge domains. Examples are research in climatic changes, biofuel production and environmental problems. A possible solution to the problem is the association of several computational techniques. Accordingly, this paper presents a framework to analyze, monitor and visualize climate and remote sensing data by employing methods based on fractal theory, data mining and visualization techniques. Initial experiments showed that the information and knowledge discovered from this framework can be employed to monitor sugar cane crops, helping agricultural entrepreneurs to make decisions in order to become more productive. Sugar cane is the main source to ethanol production in Brazil, and has a strategic importance for the country economy and to guarantee the Brazilian self-sufficiency in this important, renewable source of energy.
650 $aAgriculture
650 $aRemote sensing
650 $aSugarcane
650 $aAgricultura
653 $aCana-de-açúcar
653 $aDados agrometeorológicos
653 $aDados climáticos
653 $aDados de sensoriamento remoto
653 $aDados massivos
653 $aData mining
653 $aMineração de dados
653 $aSéries temporais
653 $aTécnicas de visualização
653 $aTeoria dos fractais
700 1 $aTRAINA, A. J. M.
700 1 $aSOUSA, E. P. M. de
700 1 $aZULLO JÚNIOR, J.
700 1 $aAVILA, A. M. H.
700 1 $aRODRIGUES JR. J. F.
700 1 $aTRAINA JÚNIOR. C.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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